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Paper Detail

Paper: PS-2B.20
Session: Poster Session 2B
Location: Symphony/Overture
Session Time: Friday, September 7, 19:30 - 21:30
Presentation Time:Friday, September 7, 19:30 - 21:30
Presentation: Poster
Publication: 2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania
Paper Title: Experimental And Computational Investigation of the Effects of Variable RSI on Sequence Learning
Manuscript:  Click here to view manuscript
Authors: Sneha Kummetha, Pramod Kaushik, Anuj Shukla, International Institute of Information Technology, Hyderabad, India; Bapi Raju Surampudi, International Institute of Information Technology, Hyderabad and University of Hyderabad, India
Abstract: In this study, we investigated the effects of variable Response-to-Stimulus interval (RSI) on sequence learning using both empirical and computational methods. In the empirical study, the serial reaction time task (SRT) was conducted which was followed by free generation and recognition tasks. Results showed that learning becomes explicit with increase in RSI despite its varying temporal nature. We constructed a computational model based on modified Elman network architecture to obtain a functional account of the empirical findings. The model illustrates how explicit learning could emerge due to a longer temporal window between stimuli which could potentially give insights into the mechanisms of sequence learning in variable RSI conditions.